{
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   "source": [
    "# Python API Quickstart\n",
    "\n",
    "A quick example showcasing ploomber's essential features.\n",
    "\n",
    "ploomber expressive syntax makes pipeline declarations read like blueprints that provide a full picture: not only they include which tasks to perform and in which order, but where output will be stored and in which form. Furthermore, upstream products are available to downstream tasks, this way, each product is only declared once.\n",
    "\n",
    "## Expressive syntax\n",
    "\n",
    "We start defining a few functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "from pathlib import Path\n",
    "import tempfile\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import HTML\n",
    "\n",
    "from ploomber import DAG\n",
    "from ploomber.tasks import PythonCallable\n",
    "from ploomber.products import File\n",
    "\n",
    "# we declare two functions one to get data\n",
    "# and another one to clean it\n",
    "\n",
    "\n",
    "def get(product):\n",
    "    \"\"\"Get data\"\"\"\n",
    "    df = pd.DataFrame({\"column\": np.random.rand(100)})\n",
    "    df.to_csv(str(product))\n",
    "\n",
    "\n",
    "def clean(upstream, product):\n",
    "    \"\"\"Clean data\"\"\"\n",
    "    data = pd.read_csv(str(upstream[\"get\"]))\n",
    "    clean = data[data.column >= 0.5]\n",
    "    clean.to_csv(str(product))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Create a DAG which will hold all our tasks and instantiate Task objects using our functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "# tmp directory to save our data\n",
    "tmp_dir = Path(tempfile.mkdtemp())\n",
    "\n",
    "# create a DAG object to organize all the tasks\n",
    "dag = DAG()\n",
    "\n",
    "# create tasks from our functions\n",
    "task_get = PythonCallable(\n",
    "    get,\n",
    "    # where to save output\n",
    "    product=File(tmp_dir / \"data.csv\"),\n",
    "    dag=dag,\n",
    ")\n",
    "\n",
    "task_clean = PythonCallable(clean, product=File(tmp_dir / \"clean.csv\"), dag=dag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": "2020-03-16T21:43:45.374708",
     "exception": false,
     "start_time": "2020-03-16T21:43:45.352471",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Declare how our tasks relate to each other:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "duration": 0.031211,
     "end_time": "2020-03-16T21:43:45.431347",
     "exception": false,
     "start_time": "2020-03-16T21:43:45.400136",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PythonCallable: clean -> File(/var/folders/3h/_lvh_w_x5g30rrjzb_xnn2j80000gq/T/tmpf0s8ypu8/clean.csv)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_get >> task_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Standalone execution\n",
    "\n",
    "ploomber's pipelines are ready to run right after being created, no need to setup a separate system. Since all products are part of the declaration, one can switch them entirely to isolate executions depending on the environment (e.g. save all output in `/data/{{user}}` where user is the current logged-in user).\n",
    "\n",
    "Before building the pipeline, let's get a summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "exception": false,
     "start_time": "2020-03-16T21:43:45.498867",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
      ]
     },
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     "output_type": "display_data"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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       "<table>\n",
       "<thead>\n",
       "<tr><th>name  </th><th>Last updated    </th><th>Outdated dependencies  </th><th>Outdated code  </th><th>Product                                                               </th><th>Doc (short)  </th><th>Location                         </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>get   </td><td>Has not been run</td><td>False                  </td><td>True           </td><td>/var/folders/3h/_lvh_w_x5g30rrjzb_xnn2j80000gq/T/tmpf0s8ypu8/data.csv </td><td>Get data     </td><td><ipython-input-3-97bc00c66958>:15</td></tr>\n",
       "<tr><td>clean </td><td>Has not been run</td><td>True                   </td><td>True           </td><td>/var/folders/3h/_lvh_w_x5g30rrjzb_xnn2j80000gq/T/tmpf0s8ypu8/clean.csv</td><td>Clean data   </td><td><ipython-input-3-97bc00c66958>:21</td></tr>\n",
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       "</table>"
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       "name    Last updated      Outdated dependencies    Outdated code    Product                                                                 Doc (short)    Location\n",
       "------  ----------------  -----------------------  ---------------  ----------------------------------------------------------------------  -------------  ---------------------------------\n",
       "get     Has not been run  False                    True             /var/folders/3h/_lvh_w_x5g30rrjzb_xnn2j80000gq/T/tmpf0s8ypu8/data.csv   Get data       <ipython-input-3-97bc00c66958>:15\n",
       "clean   Has not been run  True                     True             /var/folders/3h/_lvh_w_x5g30rrjzb_xnn2j80000gq/T/tmpf0s8ypu8/clean.csv  Clean data     <ipython-input-3-97bc00c66958>:21"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dag.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "papermill": {
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     "start_time": "2020-03-16T21:43:45.667995",
     "status": "completed"
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    {
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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       "<table>\n",
       "<thead>\n",
       "<tr><th>name  </th><th>Ran?  </th><th style=\"text-align: right;\">  Elapsed (s)</th><th style=\"text-align: right;\">  Percentage</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>get   </td><td>True  </td><td style=\"text-align: right;\">     0.124395</td><td style=\"text-align: right;\">     51.3855</td></tr>\n",
       "<tr><td>clean </td><td>True  </td><td style=\"text-align: right;\">     0.117687</td><td style=\"text-align: right;\">     48.6145</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
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       "name    Ran?      Elapsed (s)    Percentage\n",
       "------  ------  -------------  ------------\n",
       "get     True         0.124395       51.3855\n",
       "clean   True         0.117687       48.6145"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dag.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## Incremental\n",
    "\n",
    "Pipelines usually take hours or even days to run, during the development phase, it is wasteful to re-execute the pipeline on each change. ploomber keeps track of code changes and only executes a task if the source code has changed since its last execution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "papermill": {
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     "exception": false,
     "start_time": "2020-03-16T21:43:46.088981",
     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
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       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
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     },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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       "model_id": "b6e3691696ba49afae1ee1c906d79149",
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       "version_minor": 0
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       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>name  </th><th>Ran?  </th><th style=\"text-align: right;\">  Elapsed (s)</th><th style=\"text-align: right;\">  Percentage</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>get   </td><td>False </td><td style=\"text-align: right;\">            0</td><td style=\"text-align: right;\">           0</td></tr>\n",
       "<tr><td>clean </td><td>False </td><td style=\"text-align: right;\">            0</td><td style=\"text-align: right;\">           0</td></tr>\n",
       "</tbody>\n",
       "</table>"
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      "text/plain": [
       "name    Ran?      Elapsed (s)    Percentage\n",
       "------  ------  -------------  ------------\n",
       "get     False               0             0\n",
       "clean   False               0             0"
      ]
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "dag.build()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.026404,
     "end_time": "2020-03-16T21:43:46.485672",
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     "start_time": "2020-03-16T21:43:46.459268",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Testable\n",
    "\n",
    "ploomber also supports a hook to execute code upon task execution. This allows to write acceptance tests that explicitely state input assumptions (e.g. check a data frame's input schema)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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     "exception": false,
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     "status": "completed"
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   "outputs": [],
   "source": [
    "def test_no_nas(task):\n",
    "    print(\"Testing there are no NAs...\")\n",
    "    path = str(task.product)\n",
    "    df = pd.read_csv(path)\n",
    "    assert not df.column.isna().sum()\n",
    "\n",
    "\n",
    "task_get.on_finish = test_no_nas\n",
    "task_clean.on_finish = test_no_nas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "papermill": {
     "duration": 0.356068,
     "end_time": "2020-03-16T21:43:46.932637",
     "exception": false,
     "start_time": "2020-03-16T21:43:46.576569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "model_id": "fd08e20d9da14be8928b85b5f9f97d5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6fb8b9b798ec413581228065e9de2880",
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       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing there are no NAs...\n",
      "Testing there are no NAs...\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead>\n",
       "<tr><th>name  </th><th>Ran?  </th><th style=\"text-align: right;\">  Elapsed (s)</th><th style=\"text-align: right;\">  Percentage</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>get   </td><td>True  </td><td style=\"text-align: right;\">     0.123876</td><td style=\"text-align: right;\">     50.3389</td></tr>\n",
       "<tr><td>clean </td><td>True  </td><td style=\"text-align: right;\">     0.122208</td><td style=\"text-align: right;\">     49.6611</td></tr>\n",
       "</tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "name    Ran?      Elapsed (s)    Percentage\n",
       "------  ------  -------------  ------------\n",
       "get     True         0.123876       50.3389\n",
       "clean   True         0.122208       49.6611"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ignore status and force execution on all tasks\n",
    "# so we also run on_finish\n",
    "dag.build(force=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.028289,
     "end_time": "2020-03-16T21:43:46.988344",
     "exception": false,
     "start_time": "2020-03-16T21:43:46.960055",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "papermill": {
     "duration": 0.037307,
     "end_time": "2020-03-16T21:43:47.055507",
     "exception": false,
     "start_time": "2020-03-16T21:43:47.018200",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'get': PythonCallable: get -> File(/var/folders/3h/_lvh_w_x5g30rrjzb_xnn2j80000gq/T/tmpf0s8ypu8/data.csv)}\n"
     ]
    }
   ],
   "source": [
    "# get task named \"get\"\n",
    "task = dag[\"clean\"]\n",
    "\n",
    "# which are their upstream dependencies?\n",
    "print(task.upstream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "papermill": {
     "duration": 0.051113,
     "end_time": "2020-03-16T21:43:47.135354",
     "exception": false,
     "start_time": "2020-03-16T21:43:47.084241",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing there are no NAs...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TaskStatus.Executed: 'executed'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only execute this task instead of the entire dag\n",
    "task.build(force=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.360694,
     "end_time": "2020-03-16T21:43:47.591836",
     "exception": true,
     "start_time": "2020-03-16T21:43:47.231142",
     "status": "failed"
    },
    "tags": []
   },
   "source": [
    "Sstart a debugging session using pdb (only works if task is a PythonCallable)\n",
    "\n",
    "```python\n",
    "task.debug()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "end_time": null,
     "exception": null,
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     "status": "pending"
    },
    "tags": []
   },
   "source": [
    "## Communicable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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     "duration": null,
     "end_time": null,
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     "start_time": null,
     "status": "pending"
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    "tags": []
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   "outputs": [
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    {
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     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
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    },
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       "    <h1>DAG report</h1>\n",
       "<h2>Plot</h2>\n",
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       "\"></img></p>\n",
       "<h2>Status</h2>\n",
       "<table>\n",
       "<thead>\n",
       "<tr><th>name  </th><th>Last updated                       </th><th>Outdated dependencies  </th><th>Outdated code  </th><th>Product                                                               </th><th>Doc (short)  </th><th>Location                         </th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "<tr><td>get   </td><td>3 seconds ago (Mar 16, 20 at 17:45)</td><td>False                  </td><td>False          </td><td>/var/folders/3h/_lvh_w_x5g30rrjzb_xnn2j80000gq/T/tmpf0s8ypu8/data.csv </td><td>Get data     </td><td><ipython-input-3-97bc00c66958>:15</td></tr>\n",
       "<tr><td>clean </td><td>3 seconds ago (Mar 16, 20 at 17:45)</td><td>False                  </td><td>False          </td><td>/var/folders/3h/_lvh_w_x5g30rrjzb_xnn2j80000gq/T/tmpf0s8ypu8/clean.csv</td><td>Clean data   </td><td><ipython-input-3-97bc00c66958>:21</td></tr>\n",
       "</tbody>\n",
       "</table><h2>Source code</h2>\n",
       "<h3>get</h3>\n",
       "\n",
       "<pre><code>def get(product):\n",
       "    \"\"\"Get data\n",
       "    \"\"\"\n",
       "    df = pd.DataFrame({'column': np.random.rand(100)})\n",
       "    df.to_csv(str(product))</code></pre>\n",
       "<h3>clean</h3>\n",
       "\n",
       "<pre><code>def clean(upstream, product):\n",
       "    \"\"\"Clean data\n",
       "    \"\"\"\n",
       "    data = pd.read_csv(str(upstream['get']))\n",
       "    clean = data[data.column &gt;= 0.5]\n",
       "    clean.to_csv(str(product))</code></pre>\n",
       "\n",
       "</article>"
      ],
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      ]
     },
     "execution_count": 14,
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    "html = dag.to_markup()\n",
    "HTML(html)"
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   "source": [
    "import shutil  # noqa\n",
    "\n",
    "shutil.rmtree(tmp_dir)"
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